| --- |
| license: apache-2.0 |
| library_name: peft |
| tags: |
| - trl |
| - sft |
| - generated_from_trainer |
| base_model: mistralai/Mistral-7B-Instruct-v0.2 |
| model-index: |
| - name: learn-python-easy-v2 |
| results: [] |
| pipeline_tag: question-answering |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # learn-python-easy-v2 |
|
|
| This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on a samll dataset of 205 examples containing question and answer pairs regarding the Python Programming language for purposes of fine tuning experimentation. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.7009 |
|
|
| ## Model description |
|
|
| More information needed |
|
|
| ## Intended uses & limitations |
|
|
| This is intended to be used for experimental purposes regarding fine tuning of large language models and can be optimised for better outputs with more training examples. |
|
|
| ## Training and evaluation data |
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|
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 0.0001 |
| - train_batch_size: 1 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_steps: 0.03 |
| - num_epochs: 20 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | |
| |:-------------:|:-----:|:----:|:---------------:| |
| | 0.6791 | 1.0 | 164 | 0.6197 | |
| | 0.3764 | 2.0 | 328 | 0.5916 | |
| | 0.2089 | 3.0 | 492 | 0.6093 | |
| | 0.1416 | 4.0 | 656 | 0.6849 | |
| | 0.1185 | 5.0 | 820 | 0.7009 | |
| |
| |
| ### Framework versions |
| |
| - PEFT 0.10.0 |
| - Transformers 4.39.3 |
| - Pytorch 2.2.1+cu121 |
| - Datasets 2.18.0 |
| - Tokenizers 0.15.2 |
